Is it feasible to use GAN for high-quality image synthesis other than human faces?
Generative Adversarial Networks, basically boil down to a combination of a generic Generator and a Discriminator trying to beat each other, so that the generator tries to generate much better images (usually from noise) and discriminator becomes much better at classification. So, no it is not just suited for only synthesis high quality human face synthesis but any image type. 

In fact, not only can it be used for any high quality image synthesis, it can work on non-image data types as well (such as text, etc). It all depends on the type of neural network you are using for the discriminator and generator at the end of the day.

http://openaccess.thecvf.com/content_iccv_2017/html/Osokin_GANs_for_Biological_ICCV_2017_paper.html

The above refers to a paper synthesizing cell image through GANs, as I haven't personally used GANs on a practical level.

General blog explaining GANs:
https://machinelearningmastery.com/impressive-applications-of-generative-adversarial-networks/

Human faces are much more frequently tackled, for many reasons, usually that human faces are highly symmetric and have a lot of different features, usually more than other types of images, with the added difficulty of being that we as actual humans are usually good at recognizing faces - making a neural net to fool ourselves, makes it a challenging area of research.

Hope it helped! Do let me know if I'm wrong somewhere.